One concern that many people have is that player rating systems are often too general. I’ll be the first to tell you my Composite Score rating system needs a bunch of contextual information to truly be useful. It’s simply too hard to sum up all of a player’s abilities with a single number. One major problem is all the things that go unmeasured, although that’s outside the scope of our abilities until we start tracking new things.

A second major problem, one that I’m trying to find a solution for, is that different teams have different needs for different situations. Let’s say Shaquille O’Neal rates better than Rashard Lewis in Player Rating System X. The Magic should try to swap Lewis for Shaq, then, right? Obviously not. Orlando needs a big man (calling Lewis “big” is a stretch, but go along with it for now) that can stretch the floor and give space for Dwight Howard down low. Suddenly, we’re doing so much contextual research for Player Rating System X that the player rating itself isn’t that useful anymore. Instead, we’re relying on shooting percentages, shooting tendencies, rebounding ability, defensive ability, etc.

It still would be nice to have one number when we’re trying to evaluate players, if for no other reason than to save time. But we’ve already proven that one number is useless without context. What can we do?

Create multiple sets of player ratings. Better yet, create an organic player rating system that adjusts based on whatever is important to us at the moment. The Magic need a power forward that can shoot three-pointers efficiently and create his own shot from time to time? Ok, let’s rate power forwards based on that.

The next step is to calculate all of those little components and adjust them by position. Why adjust for position? If we made a player rating system based on three-point shooting ability and shot-creation alone, without adjusting for position, our numbers would tell us the Magic should acquire someone like Roger Mason and put him at power forward. That doesn’t seem like a wise suggestion.

Once we have all the position-adjusted components, we can then decide which are important based on our needs. Today is the first step. Similar to how I broke down individual players by quarter, each player in the league will be rated based on how he performs from four shooting locations: close (dunks and layups), midrange (including post shots), three-pointers, and getting to the free throw line. Each rating is adjusted for position, so a center with a 90 rating on three-pointers is still very likely worse overall than a shooting guard with an 80.

The ratings will combine both frequency and efficiency. In other words, if a player rarely shoots from midrange but is efficient at it, he won’t rate that well. Similarly, if he shoots from midrange all the time but is highly inefficient, he also won’t rate well. Ratings are on a scale of 1 to 100, with 50 being average for that position.

Frequency is measured by the player’s attempts from that shot location divided by his total attempts. Efficiency is measured by his makes divided by his total attempts from that location. The only situation that is included in this efficiency measure is when a shot actually goes up, so things like turnovers are ignored.

Before I release the numbers, I should say that these shooting tendencies and efficiencies are nothing new. 82games.com has had this data available for a while now. My methods for extracting these tendencies and efficiencies from the play-by-play data are slightly different, but they are similar. The new step I am taking is adjusting these numbers by position and creating a rating system off of these adjustments. The numbers are available through Google Docs below:

If you’re angry because a certain player does not rate the way you’d expect, allow me to explain. First, remember these ratings account for efficiency. Superstars may be excellent shot producers (a skill I will rate in the near future), but they are not always the most efficient. Second, these ratings also account for a player’s tendencies. If a player is extremely likely to take a certain shot, his rating will be high for that. However, if he balances his shot attempts, he will not rate extremely high in any of them.

A simple way to look at it is that these ratings are attempting to describe players as much as they are attempting to evaluate them. LeBron James may only get an 80 in close shots (which is still quite high), but that’s because he mixes up his attempts. He clearly is one of the most frightening players in the world when he’s near the basket.

These ratings do evaluate to an extent, but the bulk of evaluation for my new rating system will come from other components. Shooting ratings will be a big part of the context I mentioned at the beginning of this article.

This is just a first run, so changes will inevitably be made. If you have any suggestions, feel free to comment below.

Different from this but in pursuit of the same larger goal would be a lineup calculator where you’d load 4 (or maybe less) players then look at how the last player(s) affect the 4 factors offense and defense and the net efficiency, perhaps assuming constant returns and no indirect team effects though uou could add offensive and defensive team effects if you calculated them as the difference between the offensive and defensive Adjusted +/- estimates and the direct statistical impacts on each side of the ball.

The coach or GM or armchair GM could then tinker with the last player or player depending on what kind of team profile they are going for and find out which last player fits best, on the team yet or not. Really to the extent that some lineups are dictated by the need to use depth you could vary your “style” preferences and maximize the net return rather than necessarily staying rigidly consistent to one philosophy for all lineups.

Crow writes:

September 25th, 2009 at 6:13 pm

This is in the same vein as what Brett is doing at Queen City Hoops of course but setting it up for lineups and lineup manipulation would be even better and handling team defensive impact in some fashion using adjusted appears necessary to me. You could build in your shooting geometry element too and that would be appropriate and very handy.

Jon Nichols writes:

September 25th, 2009 at 6:29 pm

That is a great idea. I’m not sure what kind of statistical techniques behind the scenes would be required, or if I’m capable of using those techniques, but I’ll look into it.

Crow writes:

September 25th, 2009 at 6:53 pm

Alright, thanks.

It occurred to me you could also look at perhaps minute weighted team rankings for your shot types. The rankings would be somewhat different than the raw stat rankings and usefully combine the 2 parts of frequency and efficiency.

Or perhaps lineups by shot rank sets for the 5 man sequences and see how the different profile groupings (have to group to some level of simplicity) correlate with lineup Adjusted Offensive +/-. But we don’t have lineup Adjusted Offensive +/- yet. But perhaps it could be estimated in some fashion or successfully requested or done.

Crow writes:

September 25th, 2009 at 6:55 pm

The raw lineup offensive efficiency is available and at least provides a rough starting point.

Crow writes:

September 25th, 2009 at 6:58 pm

I did something essentially the same at your shooting method privately but didn’t convert to a 1-100 scale and that does help comparisons, especially quick ones.

Crow writes:

September 25th, 2009 at 7:00 pm

With mid-range you could offer an alternative scale where shooting it frequently is bad or progressively bad depending on how bad you shoot it.

Crow writes:

September 25th, 2009 at 7:02 pm

Your mid-range ranking based on what much in total net value your mid-range shots departed from league average eFG% for that amount of mid-range shots. That is their mid-range shot “cost” impact.